QD-LLM applies neuroevolution to prompt embeddings within a quality-diversity framework, producing 46% higher coverage and 41% higher QD-score than QDAIF on HumanEval, MBPP, and creative writing benchmarks.
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TRUST-TAEA extends two-archive evolutionary algorithms with trustworthiness-guided coordination and variable-grouping for improved convergence, diversity, and stability on LSMOPs with 500-5000 variables.
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Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
QD-LLM applies neuroevolution to prompt embeddings within a quality-diversity framework, producing 46% higher coverage and 41% higher QD-score than QDAIF on HumanEval, MBPP, and creative writing benchmarks.
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TRUST-TAEA: A trustworthiness-guided two-archive evolutionary algorithm with variable-grouping sparse search for large-scale multi-objective optimization
TRUST-TAEA extends two-archive evolutionary algorithms with trustworthiness-guided coordination and variable-grouping for improved convergence, diversity, and stability on LSMOPs with 500-5000 variables.